Common themes contributing to recent drinking water disease outbreaks in affluent nations
Bibliographic record
Abstract
Abstract New Zealand experienced its largest waterborne disease outbreak in modern history in August 2016 with 5,500 cases and four fatalities. This recent outbreak is one of 24 drinking-waterborne disease outbreaks in affluent nations that have been reported in the scientific literature since the infamous Walkerton, Ontario, Canada fatal outbreak (2,300 cases, seven fatalities) in May 2000. These disasters were all eminently preventable given the economic and intellectual resources existing in the countries where they occurred. These outbreaks are analysed according to major recurring themes, including: complacency, naiveté and ignorance, failure to learn from experience and chemophobia. Lessons that can be learned to improve preventive approaches for ensuring safe drinking water are based on an extensive and authentic body of evidence in support of meaningful improvements. Philosopher George Santayana captured this need with his famous quote: ‘Those who cannot remember the past are condemned to repeat it.’
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".